A Shift Towards an Immature Myeloid Profile in Peripheral Blood of Critically Ill COVID-19 Patients

Abstract

Background: SARS-CoV-2, the etiological agent causing COVID-19, has infected more than 27 million people with over 894000 deaths worldwide since its emergence in December 2019. Factors for severe diseases, such as diabetes, hypertension, and obesity have been identified however, the precise pathogenesis is poorly understood. To understand its pathophysiology and to develop effective therapeutic strategies, it is essential to define the prevailing immune cellular subsets.

Methods: We performed whole circulating immune cells scRNAseq from five critically ill COVID-19 patients, trajectory and gene ontology analysis.

Results: Immature myeloid populations, such as promyelocytes-myelocytes, metamyelocytes, band neutrophils, monocytoid precursors, and activated monocytes predominated. The trajectory with pseudotime analysis supported the finding of immature cell states. While the gene ontology showed myeloid cell activation in immune response, DNA and RNA processing, defense response to the virus, and response to type 1 interferon. Lymphoid lineage was scarce. Expression of genes such as C/EBPβ, IRF1and FOSL2 potentially suggests the induction of trained immunity.

Conclusions: Our results uncover transcriptomic profiles related to immature myeloid lineages and suggest the potential induction of trained immunity.

Keywords: COVID-19; Critically ill; Emergency myelopoiesis; Immune cell profile; SARS-CoV-2; Trained immunity; scRNAseq.

Figure 1

Cell populations identified in critically ill COVID-19 patients. Panel A. depicts the t-SNE from COVID-19 critically ill patients scRNAseq data. Twelve clusters are represented by immature myeloid populations such as band neutrophils, metamyelocytes, promyelocytes-myelocytes, monocytoid precursor, immature monocytes. Mature lineages such as segmented neutrophils, mature monocytes and finally monocyte-macrophages are observed. Scarce lymphoid cell populations, represented by B, T and NK cells are present in these patients. In contrast, panel B. shows the t-SNE from scRNAseq data from healthy donors with lymphoid T, Th1, NK and B cells predominance compared to monocytes.

Figure 2

Molecular markers identifying cell clusters. Panel A. portrays the hierarchical cluster from the differentially expressed genes among the cell populations identified in the COVID-19 patients. Panels B, C, and D. shows the t-SNE displaying expression of S100A9 and S100A8, ITGAM and ITGAL, respectively, in myeloid cell subsets depicting immature features. Whereas panels E, F, and G. Shows mature monocyte cell subset expressing CD14, LYZ and S100A9 genes, B cells expressing CD79A, CD79B, CD19 and NK cells expressing NKG7 and GZMA, respectively.

Figure 3

Trajectory analysis. Panel A. Trajectory analysis with pseudotime represented potential transitional states. Node 2 gathers the immature cell populations and showed time 0, follow throughout the trajectory path to node 1 were it bifurcates into 2 nodes, the second bifurcation showed the mature or differentiated cell states. Panel B. depicts the cell populations identified by their transcriptomes and their potential transitional states according to pseudotime. As expected, metamyelocytes, promyelocytes-myelocytes and immature monocytes are in node 2 and mature monocytes and B and T lymphocytes, along with NK cells were among the most mature cells in the opposite node.

Figure 4

Gene Ontology terms. Gene ontology results in myeloid cell subsets are represented in panel A. myeloid cell activation in immune response as well as granulocyte and leukocyte activation, B. Iron metabolism and anion homeostasis, C. Cell cycle control, DNA and RNA processing and D) defense response to virus, response to type 1 interferon and innate immune response.

Figure 5

Immunity trained gene expression. Violin plots show gene expression of CEBPβ, IRF1, FOSL2 and ATF3 in the critically ill COVID-19 patients immune cells analyzed in panels A, B, C, and D respectively.

Figure 5

Immunity trained gene expression. Violin plots show gene expression of CEBPβ, IRF1, FOSL2 and ATF3 in the critically ill COVID-19 patients immune cells analyzed in panels A, B, C, and D respectively.